Improving the time-machine: estimating date of birth of grade II gliomas

Objectives:  Here we present a model aiming to provide an estimate of time from tumour genesis, for grade II gliomas. The model is based on a differential equation describing the diffusion–proliferation process. We have applied our model to situations where tumour diameter was shown to increase line...

Full description

Saved in:
Bibliographic Details
Published inCell proliferation Vol. 45; no. 1; pp. 76 - 90
Main Authors Gerin, C., Pallud, J., Grammaticos, B., Mandonnet, E., Deroulers, C., Varlet, P., Capelle, L., Taillandier, L., Bauchet, L., Duffau, H., Badoual, M.
Format Journal Article
LanguageEnglish
Published Oxford, UK Blackwell Publishing Ltd 01.02.2012
Subjects
Online AccessGet full text

Cover

Loading…
Abstract Objectives:  Here we present a model aiming to provide an estimate of time from tumour genesis, for grade II gliomas. The model is based on a differential equation describing the diffusion–proliferation process. We have applied our model to situations where tumour diameter was shown to increase linearly with time, with characteristic diametric velocity. Materials and methods:  We have performed numerical simulations to analyse data, on patients with grade II gliomas and to extract information concerning time of tumour biological onset, as well as radiology and distribution of model parameters. Results and conclusions:  We show that the estimate of tumour onset obtained from extrapolation using a constant velocity assumption, always underestimates biological tumour age, and that the correction one should add to this estimate is given roughly by 20/v (year), where v is the diametric velocity of expansion of the tumour (expressed in mm/year). Within the assumptions of the model, we have identified two types of tumour: the first corresponds to very slowly growing tumours that appear during adolescence, and the second type corresponds to slowly growing tumours that appear later, during early adulthood. That all these tumours become detectable around a mean patient age of 30 years could be interesting for formulation of strategies for early detection of tumours.
AbstractList Here we present a model aiming to provide an estimate of time from tumour genesis, for grade II gliomas. The model is based on a differential equation describing the diffusion-proliferation process. We have applied our model to situations where tumour diameter was shown to increase linearly with time, with characteristic diametric velocity. We have performed numerical simulations to analyse data, on patients with grade II gliomas and to extract information concerning time of tumour biological onset, as well as radiology and distribution of model parameters. We show that the estimate of tumour onset obtained from extrapolation using a constant velocity assumption, always underestimates biological tumour age, and that the correction one should add to this estimate is given roughly by 20/v (year), where v is the diametric velocity of expansion of the tumour (expressed in mm/year). Within the assumptions of the model, we have identified two types of tumour: the first corresponds to very slowly growing tumours that appear during adolescence, and the second type corresponds to slowly growing tumours that appear later, during early adulthood. That all these tumours become detectable around a mean patient age of 30 years could be interesting for formulation of strategies for early detection of tumours.
Objectives:  Here we present a model aiming to provide an estimate of time from tumour genesis, for grade II gliomas. The model is based on a differential equation describing the diffusion–proliferation process. We have applied our model to situations where tumour diameter was shown to increase linearly with time, with characteristic diametric velocity. Materials and methods:  We have performed numerical simulations to analyse data, on patients with grade II gliomas and to extract information concerning time of tumour biological onset, as well as radiology and distribution of model parameters. Results and conclusions:  We show that the estimate of tumour onset obtained from extrapolation using a constant velocity assumption, always underestimates biological tumour age, and that the correction one should add to this estimate is given roughly by 20/v (year), where v is the diametric velocity of expansion of the tumour (expressed in mm/year). Within the assumptions of the model, we have identified two types of tumour: the first corresponds to very slowly growing tumours that appear during adolescence, and the second type corresponds to slowly growing tumours that appear later, during early adulthood. That all these tumours become detectable around a mean patient age of 30 years could be interesting for formulation of strategies for early detection of tumours.
OBJECTIVESHere we present a model aiming to provide an estimate of time from tumour genesis, for grade II gliomas. The model is based on a differential equation describing the diffusion-proliferation process. We have applied our model to situations where tumour diameter was shown to increase linearly with time, with characteristic diametric velocity. MATERIALS AND METHODSWe have performed numerical simulations to analyse data, on patients with grade II gliomas and to extract information concerning time of tumour biological onset, as well as radiology and distribution of model parameters. RESULTS AND CONCLUSIONSWe show that the estimate of tumour onset obtained from extrapolation using a constant velocity assumption, always underestimates biological tumour age, and that the correction one should add to this estimate is given roughly by 20/v (year), where v is the diametric velocity of expansion of the tumour (expressed in mm/year). Within the assumptions of the model, we have identified two types of tumour: the first corresponds to very slowly growing tumours that appear during adolescence, and the second type corresponds to slowly growing tumours that appear later, during early adulthood. That all these tumours become detectable around a mean patient age of 30 years could be interesting for formulation of strategies for early detection of tumours.
Abstract Objectives:  Here we present a model aiming to provide an estimate of time from tumour genesis, for grade II gliomas. The model is based on a differential equation describing the diffusion–proliferation process. We have applied our model to situations where tumour diameter was shown to increase linearly with time, with characteristic diametric velocity. Materials and methods:  We have performed numerical simulations to analyse data, on patients with grade II gliomas and to extract information concerning time of tumour biological onset, as well as radiology and distribution of model parameters. Results and conclusions:  We show that the estimate of tumour onset obtained from extrapolation using a constant velocity assumption, always underestimates biological tumour age, and that the correction one should add to this estimate is given roughly by 20/ v (year), where v is the diametric velocity of expansion of the tumour (expressed in mm/year). Within the assumptions of the model, we have identified two types of tumour: the first corresponds to very slowly growing tumours that appear during adolescence, and the second type corresponds to slowly growing tumours that appear later, during early adulthood. That all these tumours become detectable around a mean patient age of 30 years could be interesting for formulation of strategies for early detection of tumours.
Objectives:  Here we present a model aiming to provide an estimate of time from tumour genesis, for grade II gliomas. The model is based on a differential equation describing the diffusion–proliferation process. We have applied our model to situations where tumour diameter was shown to increase linearly with time, with characteristic diametric velocity. Materials and methods:  We have performed numerical simulations to analyse data, on patients with grade II gliomas and to extract information concerning time of tumour biological onset, as well as radiology and distribution of model parameters. Results and conclusions:  We show that the estimate of tumour onset obtained from extrapolation using a constant velocity assumption, always underestimates biological tumour age, and that the correction one should add to this estimate is given roughly by 20/ v (year), where v is the diametric velocity of expansion of the tumour (expressed in mm/year). Within the assumptions of the model, we have identified two types of tumour: the first corresponds to very slowly growing tumours that appear during adolescence, and the second type corresponds to slowly growing tumours that appear later, during early adulthood. That all these tumours become detectable around a mean patient age of 30 years could be interesting for formulation of strategies for early detection of tumours.
Author Deroulers, C.
Grammaticos, B.
Pallud, J.
Badoual, M.
Varlet, P.
Taillandier, L.
Mandonnet, E.
Duffau, H.
Bauchet, L.
Capelle, L.
Gerin, C.
AuthorAffiliation 3 Department of Neuropathology, Sainte‐Anne Hospital, Paris, France
7 Department of Neurosurgery, Pitié‐Salpétrière Hospital, Paris, France
2 Department of Neurosurgery, Sainte‐Anne Hospital, Paris, France
4 Department of Neurosurgery, Lariboisière Hospital, Paris, France
5 Department of Neuro‐Oncology, Nancy Neurological Hospital, Nancy, France
8 Réseau d’Etude des Gliomes, REG, Groland, France
1 IMNC Laboratory, Paris VII‐Paris XI Universities, CNRS, UMR 8165, Orsay, France
6 Department of Neurosurgery, CHU of Montpellier, Montpellier, France
AuthorAffiliation_xml – name: 4 Department of Neurosurgery, Lariboisière Hospital, Paris, France
– name: 6 Department of Neurosurgery, CHU of Montpellier, Montpellier, France
– name: 1 IMNC Laboratory, Paris VII‐Paris XI Universities, CNRS, UMR 8165, Orsay, France
– name: 8 Réseau d’Etude des Gliomes, REG, Groland, France
– name: 2 Department of Neurosurgery, Sainte‐Anne Hospital, Paris, France
– name: 5 Department of Neuro‐Oncology, Nancy Neurological Hospital, Nancy, France
– name: 7 Department of Neurosurgery, Pitié‐Salpétrière Hospital, Paris, France
– name: 3 Department of Neuropathology, Sainte‐Anne Hospital, Paris, France
Author_xml – sequence: 1
  givenname: C.
  surname: Gerin
  fullname: Gerin, C.
  organization: IMNC Laboratory, Paris VII-Paris XI Universities, CNRS, UMR 8165, Orsay, France
– sequence: 2
  givenname: J.
  surname: Pallud
  fullname: Pallud, J.
  organization: Department of Neurosurgery, Sainte-Anne Hospital, Paris, France
– sequence: 3
  givenname: B.
  surname: Grammaticos
  fullname: Grammaticos, B.
  organization: IMNC Laboratory, Paris VII-Paris XI Universities, CNRS, UMR 8165, Orsay, France
– sequence: 4
  givenname: E.
  surname: Mandonnet
  fullname: Mandonnet, E.
  organization: IMNC Laboratory, Paris VII-Paris XI Universities, CNRS, UMR 8165, Orsay, France
– sequence: 5
  givenname: C.
  surname: Deroulers
  fullname: Deroulers, C.
  organization: IMNC Laboratory, Paris VII-Paris XI Universities, CNRS, UMR 8165, Orsay, France
– sequence: 6
  givenname: P.
  surname: Varlet
  fullname: Varlet, P.
  organization: Department of Neuropathology, Sainte-Anne Hospital, Paris, France
– sequence: 7
  givenname: L.
  surname: Capelle
  fullname: Capelle, L.
  organization: Department of Neurosurgery, Pitié-Salpétrière Hospital, Paris, France
– sequence: 8
  givenname: L.
  surname: Taillandier
  fullname: Taillandier, L.
  organization: Department of Neuro-Oncology, Nancy Neurological Hospital, Nancy, France
– sequence: 9
  givenname: L.
  surname: Bauchet
  fullname: Bauchet, L.
  organization: Department of Neurosurgery, CHU of Montpellier, Montpellier, France
– sequence: 10
  givenname: H.
  surname: Duffau
  fullname: Duffau, H.
  organization: Department of Neurosurgery, CHU of Montpellier, Montpellier, France
– sequence: 11
  givenname: M.
  surname: Badoual
  fullname: Badoual, M.
  organization: IMNC Laboratory, Paris VII-Paris XI Universities, CNRS, UMR 8165, Orsay, France
BackLink https://www.ncbi.nlm.nih.gov/pubmed/22168136$$D View this record in MEDLINE/PubMed
BookMark eNqNUV1P2zAUtRATFLa_gPK2p2T-SGxnmiZNFdBK3WAT0x6vHMdp3SVxsVNW_j0OhWp7m198rXvOucfnnqHj3vUGoYTgjMTzYZ0RxouUEplnFBOSYSxKnO2O0OTQOEYTXHKcCkHpKToLYY0xYUTwE3RKKeEyIidoNu823j3YfpkMK5MMtjNpp_TK9uZjYkJ8q2Fs1mowiWuSyvphNRZLr2qTzOfJsrWuU-EtetOoNph3L_c5-nl1eTedpYub6_n0yyLVhYhuSKVrXlRCUVVLw-uKaqYKzIQsGq0paWQhKoZzWdY5K3PFKC4Y1YKzsqkaKdg5-rzX3WyrztTa9INXLWx8dOofwSkL_3Z6u4KlewCel5xSFgXevwh4d7-NX4TOBm3aVvXGbQOUNLrBnJCIlHuk9i4Eb5rDFIJh3AOsYYwbxrhh3AM87wF2kXrxt8sD8TX4CPi0B_yxrXn8b2GY3v6IRaSne7oNg9kd6Mr_Bi6YKODXt2v4LmdTsfjK4Y49AUp-p0k
CitedBy_id crossref_primary_10_1016_j_wneu_2016_11_013
crossref_primary_10_1007_s13277_015_4515_7
crossref_primary_10_1111_cpr_12114
crossref_primary_10_1371_journal_pcbi_1006778
crossref_primary_10_1007_s00701_013_1886_7
crossref_primary_10_1016_j_lpm_2017_07_005
crossref_primary_10_3390_jpm12101621
crossref_primary_10_1016_j_cnsns_2019_01_015
crossref_primary_10_3389_fonc_2022_855587
crossref_primary_10_1002_cncr_28610
crossref_primary_10_1016_j_nec_2018_08_009
crossref_primary_10_1371_journal_pone_0179999
crossref_primary_10_1371_journal_pone_0144200
crossref_primary_10_1016_j_chemgeo_2015_10_005
crossref_primary_10_1098_rsif_2019_0665
crossref_primary_10_1007_s11075_019_00823_6
crossref_primary_10_1016_j_mbs_2017_02_003
crossref_primary_10_1016_S0001_4079_19_30504_7
crossref_primary_10_1007_s13160_015_0188_2
crossref_primary_10_1016_j_wneu_2014_03_024
crossref_primary_10_1016_j_wneu_2012_06_036
crossref_primary_10_3390_jpm11080818
crossref_primary_10_1093_imammb_dqv017
crossref_primary_10_1371_journal_pone_0102499
crossref_primary_10_1007_s10143_015_0675_6
crossref_primary_10_1098_rsif_2017_0490
crossref_primary_10_1371_journal_pcbi_1005977
crossref_primary_10_1016_j_mbs_2015_05_006
crossref_primary_10_1016_j_neurol_2023_01_724
crossref_primary_10_1142_S1793048017500047
crossref_primary_10_1016_j_cnsns_2014_02_004
crossref_primary_10_1016_j_critrevonc_2016_11_014
crossref_primary_10_1016_j_cnsns_2017_02_008
crossref_primary_10_1093_neuonc_not072
Cites_doi 10.1016/j.jtbi.2007.10.026
10.1111/j.1365-2184.1995.tb00036.x
10.1097/nen.0b013e31802d9000
10.1111/j.1365-2184.2010.00703.x
10.1088/0031-9155/55/12/001
10.1103/PhysRevE.79.031917
10.1007/s00701-010-0917-x
10.1111/j.1365-2184.1996.tb01580.x
10.1111/j.1365-2184.2009.00631.x
10.1007/s00401-007-0243-4
10.1227/01.neu.0000318159.21731.cf
10.1200/JCO.2003.05.063
10.1212/WNL.0b013e3181e04264
10.1142/S0218339095000836
10.1002/ana.21125
10.1046/j.1365-2184.2000.00177.x
10.1007/s11060-009-9991-4
10.1088/1478-3975/3/2/001
10.2174/157488706778250140
10.1093/neuonc/noq055
10.1002/mrm.20625
10.1088/0031-9155/52/11/023
10.3171/jns.2001.95.5.0735
10.1006/bulm.1998.0042
10.1088/1478-3975/7/4/046013
10.1158/0008-5472.CAN-05-3166
10.1007/s10143-009-0229-x
10.1088/1478-3975/4/4/P01
10.1023/A:1005707203596
10.1215/15228517-2008-066
10.1007/b98868
10.1088/0031-9155/53/4/004
10.3171/jns.1987.66.6.0865
10.1002/ana.22106
10.1098/rsif.2007.1070
10.1002/ana.20946
10.1007/s10143-008-0128-6
10.1002/ana.10528
10.1097/00005072-199706000-00008
10.1016/j.ncl.2010.03.022
ContentType Journal Article
Copyright 2011 Blackwell Publishing Ltd
2011 Blackwell Publishing Ltd.
Copyright_xml – notice: 2011 Blackwell Publishing Ltd
– notice: 2011 Blackwell Publishing Ltd.
DBID BSCLL
CGR
CUY
CVF
ECM
EIF
NPM
AAYXX
CITATION
7X8
5PM
DOI 10.1111/j.1365-2184.2011.00790.x
DatabaseName Istex
Medline
MEDLINE
MEDLINE (Ovid)
MEDLINE
MEDLINE
PubMed
CrossRef
MEDLINE - Academic
PubMed Central (Full Participant titles)
DatabaseTitle MEDLINE
Medline Complete
MEDLINE with Full Text
PubMed
MEDLINE (Ovid)
CrossRef
MEDLINE - Academic
DatabaseTitleList MEDLINE

MEDLINE - Academic
CrossRef

Database_xml – sequence: 1
  dbid: NPM
  name: PubMed
  url: https://proxy.k.utb.cz/login?url=http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed
  sourceTypes: Index Database
– sequence: 2
  dbid: EIF
  name: MEDLINE
  url: https://proxy.k.utb.cz/login?url=https://www.webofscience.com/wos/medline/basic-search
  sourceTypes: Index Database
DeliveryMethod fulltext_linktorsrc
Discipline Biology
DocumentTitleAlternate C. Gerin et al
EISSN 1365-2184
EndPage 90
ExternalDocumentID 10_1111_j_1365_2184_2011_00790_x
22168136
CPR790
ark_67375_WNG_Q8HC7LM6_T
Genre article
Research Support, Non-U.S. Gov't
Journal Article
GroupedDBID ---
-~X
.3N
.GA
.GJ
.Y3
05W
0R~
10A
1OB
1OC
24P
29B
31~
33P
36B
3SF
4.4
50Y
50Z
51W
51X
52M
52N
52O
52P
52R
52S
52T
52U
52V
52W
52X
53G
5GY
5HH
5LA
5VS
66C
702
7PT
8-0
8-1
8-3
8-4
8-5
8UM
930
A01
A03
AAESR
AAEVG
AAHHS
AAONW
AAZKR
ABCQN
ABDBF
ABEML
ABPVW
ACBWZ
ACCFJ
ACGFS
ACIWK
ACMXC
ACPRK
ACSCC
ACXQS
ADBBV
ADEOM
ADIZJ
ADKYN
ADPDF
ADZMN
ADZOD
AEEZP
AEIMD
AENEX
AEQDE
AEUQT
AFBPY
AFEBI
AFKRA
AFPWT
AFRAH
AFZJQ
AHEFC
AI.
AIWBW
AJBDE
ALAGY
ALMA_UNASSIGNED_HOLDINGS
ALUQN
AMBMR
ATUGU
AVUZU
AZBYB
AZFZN
AZVAB
BAFTC
BBNVY
BCNDV
BDRZF
BENPR
BFHJK
BHBCM
BHPHI
BMXJE
BROTX
BRXPI
BSCLL
BY8
CAG
CCPQU
COF
CS3
D-6
D-7
D-E
D-F
DCZOG
DPXWK
DR2
DU5
EAD
EAP
EAS
EBC
EBD
EBS
EBX
EJD
EMB
EMK
EMOBN
EPT
ESX
EX3
F00
F01
F04
F5P
FEDTE
FUBAC
FZ0
G-S
G.N
GLUZI
GODZA
GROUPED_DOAJ
H.X
HCIFZ
HF~
HVGLF
HZI
HZ~
IAO
IGS
IHE
IHR
INH
ITC
IX1
J0M
K48
LC2
LC3
LH4
LITHE
LOXES
LP6
LP7
LUTES
LW6
M7P
MK4
MRFUL
MRMAN
MRSTM
MSFUL
MSMAN
MSSTM
N04
N05
N9A
NF~
O66
O9-
OIG
OK1
OVD
OVEED
P2W
P2X
P2Z
P4B
P4D
PALCI
PIMPY
Q.N
Q11
QB0
Q~Q
R.K
RIWAO
RJQFR
ROL
RPM
RX1
SAMSI
SUPJJ
SV3
TEORI
TUS
UB1
VH1
W8V
W99
WBKPD
WH7
WIB
WIH
WIJ
WIK
WIN
WNSPC
WOHZO
WOW
WQJ
WRC
WUP
WXI
WXSBR
WYISQ
XG1
ZXP
~IA
~WT
CGR
CUY
CVF
ECM
EIF
NPM
AAYXX
CITATION
7X8
5PM
ID FETCH-LOGICAL-c5760-1bcd65b7a2ad8e6db2c3a503785fcc21f857b30489d4394a320532c7639fbf873
IEDL.DBID RPM
ISSN 0960-7722
IngestDate Tue Sep 17 21:32:30 EDT 2024
Fri Aug 16 02:02:45 EDT 2024
Thu Sep 26 16:20:58 EDT 2024
Sat Sep 28 08:36:29 EDT 2024
Sat Aug 24 00:57:42 EDT 2024
Wed Oct 30 09:52:41 EDT 2024
IsDoiOpenAccess false
IsOpenAccess true
IsPeerReviewed true
IsScholarly true
Issue 1
Language English
License 2011 Blackwell Publishing Ltd.
LinkModel DirectLink
MergedId FETCHMERGED-LOGICAL-c5760-1bcd65b7a2ad8e6db2c3a503785fcc21f857b30489d4394a320532c7639fbf873
Notes ArticleID:CPR790
ark:/67375/WNG-Q8HC7LM6-T
istex:8DB593093C03D767E1E81E4D5B76F009A3338044
ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 23
OpenAccessLink https://europepmc.org/articles/pmc6496223?pdf=render
PMID 22168136
PQID 920370611
PQPubID 23479
PageCount 15
ParticipantIDs pubmedcentral_primary_oai_pubmedcentral_nih_gov_6496223
proquest_miscellaneous_920370611
crossref_primary_10_1111_j_1365_2184_2011_00790_x
pubmed_primary_22168136
wiley_primary_10_1111_j_1365_2184_2011_00790_x_CPR790
istex_primary_ark_67375_WNG_Q8HC7LM6_T
PublicationCentury 2000
PublicationDate February 2012
PublicationDateYYYYMMDD 2012-02-01
PublicationDate_xml – month: 02
  year: 2012
  text: February 2012
PublicationDecade 2010
PublicationPlace Oxford, UK
PublicationPlace_xml – name: Oxford, UK
– name: England
PublicationTitle Cell proliferation
PublicationTitleAlternate Cell Prolif
PublicationYear 2012
Publisher Blackwell Publishing Ltd
Publisher_xml – name: Blackwell Publishing Ltd
References Stein AM, Demuth T, Mobley D, Berens M, Sander LM (2007) A mathematical model of glioblastoma tumor spheroid invasion in a three-dimensional in vitro experiment. Rep. Prog. Phys. 72, 356-365.
Hlaihel C, Guilloton L, Guyotat J, Streichenberger N, Honnorat J, Cotton F (2010) Predictive value of multimodality MRI using conventional, perfusion, and spectroscopy MR in anaplastic transformation of low-grade oligodendrogliomas. J. Neurooncol. 97, 73-80.
Swanson KR, Bridge C, Murray JD, Alvord EC Jr (2004) Virtual and real brain tumors: using mathematical modeling to quantify glioma growth and invasion. J. Neurol. Sci. 216, 289-296.
Tracqui P, Cruywagen GC, Woodward DE, Bartoo GT, Murray JD, Alvord EC (1995) A mathematical model of glioma growth: the effect of chemotherapy on spatio-temporal growth. Cell Prolif. 28, 17-31.
Harpold HL, Alvord EC Jr, Swanson KR (2007) The evolution of mathematical modeling of glioma proliferation and invasion. J. Neuropathol. Exp. Neurol. 1, 1-9.
Jbabdi S, Mandonnet E, Duffau H, Capelle L, Swanson KR, Pelegrini-Issac M et al. (2005) Simulation of anisotropic growth of low-grade gliomas using diffusion tensor imaging. Magn. Reson. Med. 54, 616-624.
Keles GE, Lamborn KR, Berger MS (2001) Low-grade hemispheric gliomas in adults: a critical review of extent of resection as a factor influencing outcome. J. Neurosurg. 95, 735-745.
Pallud J, Capelle L, Taillandier L, Fontaine D, Mandonnet E, Guillevin R et al. (2009) Prognostic significance of imaging contrast enhancement for WHO grade II gliomas. Neuro-oncology 11, 176-182.
Giese A, Bjerkvig R, Berens ME, Westphal M (2003) Cost of migration: invasion of malignant gliomas and implications for treatment. J. Clin. Oncol. 21, 1624-1636.
Woodward DE, Cook J, Tracqui P, Cruywagen GC, Murray JD, Alvord EC (1996) A mathematical model of glioma growth: the effect of extent of surgical resection. Cell Prolif. 29, 269-288.
Pallud J, Varlet P, Devaux B, Geha S, Badoual M, Deroulers C et al. (2010) Diffuse low-grade oligodendrogliomas extend beyond MRI-defined abnormalities. Neurology 74, 1724-1731.
Ricard D, Kaloshi G, Amiel-Benouaich A, Lejeune J, Marie Y, Mandonnet E et al. (2007) Dynamic history of low-grade gliomas before and after temozolomide treatment. Ann. Neurol. 61, 484-490.
Gerisch A, Chaplain MA (2008) Mathematical modelling of cancer cell invasion of tissue: local and non-local models and the effect of adhesion. J. Theor. Biol. 250, 684-704.
Tanaka ML, Debinski W, Puri IK (2009) Hybrid mathematical model of glioma progression. Cell Prolif. 42, 637-646.
Pallud J, Fontaine D, Duffau H, Mandonnet E, Sanai N, Taillandier L et al. (2010) Natural history of incidental WHO grade II gliomas. Ann. Neurol. 68, 727-733.
Louis DN, Ohgaki H, Wiestler OD, Cavenee WK, Burger PC, Jouvet A et al. (2007) The 2007 WHO classification of tumours of the central nervous system. Acta Neuropathol. 114, 97-109.
Salgaller ML, Liau LM (2006) Current status of clinical trials for glioblastoma. Rev. Recent Clin. Trials 3, 265-281.
Mandonnet E, Pallud J, Clatz O, Taillandier L, Konukoglu E, Duffau H et al. (2008) Computational modeling of the who grade II glioma dynamics: principles and applications to management paradigm. Neurosurg. Rev. 31, 263-269.
Aubert M, Badoual M, Féreol S, Christov C, Grammaticos B (2006) A cellular automaton model for the migration of glioma cells. Phys. Biol. 3, 93-100.
Cook J, Woodward DE, Tracqui P, Cruywagen GC, Murray JD, Bartoo GT et al. (1995) Resection of gliomas and life expectancy. J. Neurooncol. 24, 131.
Daumas-Duport C, Varlet P, Tucker ML, Beuvon F, Cervera P, Chodkiewicz JP (1997) Oligodendrogliomas. part i: Patterns of growth, histological diagnosis, clinical and imaging correlations: a study of 153 cases. J. Neurooncol. 34, 37-59.
Kelly PJ, Daumas-Duport C, Kispert DB, Kall BA, Scheithauer W, Illig J (1987) Imaging-based stereotaxic serial biopsies in untreated intracranial glial neaplasms. J. Neurosurg. 66, 865-874.
Frieboes HB, Zheng X, Sun CH, Tromberg B, Gatenby R, Cristini V (2006) An integrated computational/experimental model of tumor invasion. Cancer Res. 66, 1597-1604.
Cooper MD, Tanaka ML, Puri IK (2010) Coupled mathematical model of tumorigenesis and angiogenesis in vascular tumours. Cell Prolif. 43, 542-552.
Duffau H, Pallud J, Mandonnet E (2011) Evidence for the genesis of WHO grade II glioma in an asymptomatic young adult using repeated MRIs. Acta Neurochir. 153, 473-477.
Aubert M, Badoual M, Christov C, Grammaticos B (2008) A model for glioma cell migration on collagen and astrocytes. J. R. Soc. Interface 5, 75-83.
Peyre M, Cartalat-Carel S, Meyronet D, Ricard D, Jouvet A, Pallud J et al. (2010) Prolonged response without prolonged chemotherapy: a lesson from PCV chemotherapy in low-grade gliomas. Neuro Oncol. 12, 1078-1082.
Burgess PK, Kulesa PM, Murray JD, Alvord EC (1997) The interaction of growth rates and diffusion coefficients in a three-dimensional mathematical model of gliomas. J. Neuropathol. Exp. Neurol. 56, 704.
Prabhu VC, Khaldi A, Barton KP, Melian E, Schneck MJ, Primeau MJ et al. (2010) Management of diffuse low-grade cerebral gliomas. Neurol. Clin. 28, 1037-1059.
Deroulers C, Aubert M, Badoual M, Grammaticos B (2009) Modeling tumor cell migration: from microscopic to macroscopic models. Phys. Rev. E 79, 031917-1-031917-14.
Cruywagen GC, Woodward DE, Tracqui P, Bartoo GT, Murray JD, Alvord EC (1995) The modelling of diffusive tumours. J. Biol. Syst. 3, 937-945.
Badoual M, Deroulers C, Aubert M, Grammaticos B (2010) Modelling intercellular communication and its effect on tumour invasion. Phys. Biol. 7, 046013.
Pallud J, Mandonnet E, Duffau H, Kujas M, Guillevin R, Galanaud D et al. (2006) Prognostic value of initial magnetic resonance imaging growth rates for world health organization grade II gliomas. Ann. Neurol. 60, 380-383.
Mandonnet E, Delattre JY, Tanguy ML, Swanson KR, Carpentier AF, Duffau H et al. (2003) Continuous growth of mean tumor diameter in a subset of grade II gliomas. Ann. Neurol. 53, 524-528.
Murray JD (2002) Mathematical Biology. II: Spatial Models and Biomedical Applications, 3rd edn. Berlin: Springer-Verlag.
Powathil G, Kohandel M, Sivaloganathan S, Oza A, Milosevic M (2007) Mathematical modeling of brain tumors: effects of radiotherapy and chemotherapy. Phys. Med. Biol. 52, 3291-3306.
Anderson AR, Chaplain MA (1998) Continuous and discrete mathematical models of tumor-induced angiogenesis. Bull. Math. Biol. 60, 857-899.
Swanson KR, Alvord EC, Murray JD (2000) A quantitative model for differential motility of gliomas in grey and white matter. Cell Prolif. 33, 317-329.
Murray JD (2002) Mathematical Biology: I. An Introduction, 3rd edn. Berlin: Springer-Verlag.
Mandonnet E, Pallud J, Fontaine D, Taillandier L, Bauchet L, Peruzzi P et al. (2009) Inter- and intrapatients comparison of WHO grade II glioma kinetics before and after surgical resection. Neurosurg. Rev. 33, 91-96.
Guiot C, Pugnot N, Delsanto PP, Deisboeck TS (2007) Physical aspects of cancer invasion. Phys. Biol. 4, 1-6.
Rockne R, Rockhill JK, Mrugala M, Spence AM, Kalet I, Hendrickson K et al. (2010) Predicting the efficacy of radiotherapy in individual glioblastoma patients in vivo: a mathematical modelling approach. Phys. Med. Biol. 55, 3271-3285.
Sanai N, Berger MS (2008) Glioma extent of resection and its impact on patient outcome. Neurosurgery 4, 753-764.
Bondiau PY, Clatz O, Sermesant M, Marcy PY, Delinguette H, Frenay M et al. (2008) Biocomputing: numerical simulation of glioblastoma growth using diffusion tensor imaging. Phys. Med. Biol. 53, 879-893.
2010; 12
2010; 55
2010; 97
2009; 42
2006; 3
2008; 5
2007; 72
2008; 4
2002
2008; 53
2007; 52
2008; 31
1998; 60
1995; 3
2003; 53
2011; 153
2007; 114
2009; 33
2006; 60
2009; 11
1987; 66
2010; 43
2009; 79
1996; 29
2010; 68
1995; 28
2010; 28
2006; 66
1995; 24
2000; 33
1997; 56
1997; 34
2005; 54
2007; 4
2007; 61
2007; 1
2004; 216
2010; 74
2001; 95
2010; 7
2003; 21
2008; 250
e_1_2_7_5_2
e_1_2_7_4_2
e_1_2_7_3_2
e_1_2_7_2_2
e_1_2_7_9_2
e_1_2_7_8_2
e_1_2_7_7_2
e_1_2_7_6_2
e_1_2_7_19_2
e_1_2_7_18_2
e_1_2_7_16_2
e_1_2_7_15_2
e_1_2_7_14_2
e_1_2_7_40_2
e_1_2_7_13_2
e_1_2_7_41_2
e_1_2_7_12_2
e_1_2_7_42_2
e_1_2_7_11_2
e_1_2_7_43_2
e_1_2_7_10_2
e_1_2_7_44_2
e_1_2_7_45_2
e_1_2_7_26_2
e_1_2_7_28_2
e_1_2_7_29_2
Murray JD (e_1_2_7_24_2) 2002
Cook J (e_1_2_7_27_2) 1995; 24
Stein AM (e_1_2_7_17_2) 2007; 72
e_1_2_7_25_2
e_1_2_7_30_2
e_1_2_7_23_2
e_1_2_7_31_2
e_1_2_7_22_2
Swanson KR (e_1_2_7_32_2) 2004; 216
e_1_2_7_21_2
e_1_2_7_33_2
e_1_2_7_20_2
e_1_2_7_34_2
e_1_2_7_35_2
e_1_2_7_36_2
e_1_2_7_37_2
e_1_2_7_38_2
e_1_2_7_39_2
References_xml – volume: 28
  start-page: 1037
  year: 2010
  end-page: 1059
  article-title: Management of diffuse low‐grade cerebral gliomas
  publication-title: Neurol. Clin.
– volume: 34
  start-page: 37
  year: 1997
  end-page: 59
  article-title: Oligodendrogliomas. part i: Patterns of growth, histological diagnosis, clinical and imaging correlations: a study of 153 cases
  publication-title: J. Neurooncol.
– volume: 54
  start-page: 616
  year: 2005
  end-page: 624
  article-title: Simulation of anisotropic growth of low‐grade gliomas using diffusion tensor imaging
  publication-title: Magn. Reson. Med.
– volume: 31
  start-page: 263
  year: 2008
  end-page: 269
  article-title: Computational modeling of the who grade II glioma dynamics: principles and applications to management paradigm
  publication-title: Neurosurg. Rev.
– volume: 5
  start-page: 75
  year: 2008
  end-page: 83
  article-title: A model for glioma cell migration on collagen and astrocytes
  publication-title: J. R. Soc. Interface
– volume: 53
  start-page: 524
  year: 2003
  end-page: 528
  article-title: Continuous growth of mean tumor diameter in a subset of grade II gliomas
  publication-title: Ann. Neurol.
– volume: 66
  start-page: 865
  year: 1987
  end-page: 874
  article-title: Imaging‐based stereotaxic serial biopsies in untreated intracranial glial neaplasms
  publication-title: J. Neurosurg.
– volume: 33
  start-page: 317
  year: 2000
  end-page: 329
  article-title: A quantitative model for differential motility of gliomas in grey and white matter
  publication-title: Cell Prolif.
– volume: 43
  start-page: 542
  year: 2010
  end-page: 552
  article-title: Coupled mathematical model of tumorigenesis and angiogenesis in vascular tumours
  publication-title: Cell Prolif.
– volume: 74
  start-page: 1724
  year: 2010
  end-page: 1731
  article-title: Diffuse low‐grade oligodendrogliomas extend beyond MRI‐defined abnormalities
  publication-title: Neurology
– volume: 61
  start-page: 484
  year: 2007
  end-page: 490
  article-title: Dynamic history of low‐grade gliomas before and after temozolomide treatment
  publication-title: Ann. Neurol.
– volume: 79
  start-page: 031917‐1
  year: 2009
  end-page: 031917‐14
  article-title: Modeling tumor cell migration: from microscopic to macroscopic models
  publication-title: Phys. Rev. E
– volume: 97
  start-page: 73
  year: 2010
  end-page: 80
  article-title: Predictive value of multimodality MRI using conventional, perfusion, and spectroscopy MR in anaplastic transformation of low‐grade oligodendrogliomas
  publication-title: J. Neurooncol.
– volume: 55
  start-page: 3271
  year: 2010
  end-page: 3285
  article-title: Predicting the efficacy of radiotherapy in individual glioblastoma patients in vivo: a mathematical modelling approach
  publication-title: Phys. Med. Biol.
– volume: 7
  start-page: 046013
  year: 2010
  article-title: Modelling intercellular communication and its effect on tumour invasion
  publication-title: Phys. Biol.
– volume: 153
  start-page: 473
  year: 2011
  end-page: 477
  article-title: Evidence for the genesis of WHO grade II glioma in an asymptomatic young adult using repeated MRIs
  publication-title: Acta Neurochir.
– volume: 12
  start-page: 1078
  year: 2010
  end-page: 1082
  article-title: Prolonged response without prolonged chemotherapy: a lesson from PCV chemotherapy in low‐grade gliomas
  publication-title: Neuro Oncol.
– volume: 250
  start-page: 684
  year: 2008
  end-page: 704
  article-title: Mathematical modelling of cancer cell invasion of tissue: local and non‐local models and the effect of adhesion
  publication-title: J. Theor. Biol.
– volume: 42
  start-page: 637
  year: 2009
  end-page: 646
  article-title: Hybrid mathematical model of glioma progression
  publication-title: Cell Prolif.
– volume: 60
  start-page: 380
  year: 2006
  end-page: 383
  article-title: Prognostic value of initial magnetic resonance imaging growth rates for world health organization grade II gliomas
  publication-title: Ann. Neurol.
– volume: 3
  start-page: 265
  year: 2006
  end-page: 281
  article-title: Current status of clinical trials for glioblastoma
  publication-title: Rev. Recent Clin. Trials
– volume: 72
  start-page: 356
  year: 2007
  end-page: 365
  article-title: A mathematical model of glioblastoma tumor spheroid invasion in a three‐dimensional in vitro experiment
  publication-title: Rep. Prog. Phys.
– volume: 68
  start-page: 727
  year: 2010
  end-page: 733
  article-title: Natural history of incidental WHO grade II gliomas
  publication-title: Ann. Neurol.
– volume: 3
  start-page: 937
  year: 1995
  end-page: 945
  article-title: The modelling of diffusive tumours
  publication-title: J. Biol. Syst.
– volume: 33
  start-page: 91
  year: 2009
  end-page: 96
  article-title: Inter‐ and intrapatients comparison of WHO grade II glioma kinetics before and after surgical resection
  publication-title: Neurosurg. Rev.
– volume: 53
  start-page: 879
  year: 2008
  end-page: 893
  article-title: Biocomputing: numerical simulation of glioblastoma growth using diffusion tensor imaging
  publication-title: Phys. Med. Biol.
– volume: 52
  start-page: 3291
  year: 2007
  end-page: 3306
  article-title: Mathematical modeling of brain tumors: effects of radiotherapy and chemotherapy
  publication-title: Phys. Med. Biol.
– volume: 4
  start-page: 1
  year: 2007
  end-page: 6
  article-title: Physical aspects of cancer invasion
  publication-title: Phys. Biol.
– year: 2002
– volume: 28
  start-page: 17
  year: 1995
  end-page: 31
  article-title: A mathematical model of glioma growth: the effect of chemotherapy on spatio‐temporal growth
  publication-title: Cell Prolif.
– volume: 60
  start-page: 857
  year: 1998
  end-page: 899
  article-title: Continuous and discrete mathematical models of tumor‐induced angiogenesis
  publication-title: Bull. Math. Biol.
– volume: 216
  start-page: 289
  year: 2004
  end-page: 296
  article-title: Virtual and real brain tumors: using mathematical modeling to quantify glioma growth and invasion
  publication-title: J. Neurol. Sci.
– volume: 66
  start-page: 1597
  year: 2006
  end-page: 1604
  article-title: An integrated computational/experimental model of tumor invasion
  publication-title: Cancer Res.
– volume: 1
  start-page: 1
  year: 2007
  end-page: 9
  article-title: The evolution of mathematical modeling of glioma proliferation and invasion
  publication-title: J. Neuropathol. Exp. Neurol.
– volume: 95
  start-page: 735
  year: 2001
  end-page: 745
  article-title: Low‐grade hemispheric gliomas in adults: a critical review of extent of resection as a factor influencing outcome
  publication-title: J. Neurosurg.
– volume: 4
  start-page: 753
  year: 2008
  end-page: 764
  article-title: Glioma extent of resection and its impact on patient outcome
  publication-title: Neurosurgery
– volume: 29
  start-page: 269
  year: 1996
  end-page: 288
  article-title: A mathematical model of glioma growth: the effect of extent of surgical resection
  publication-title: Cell Prolif.
– volume: 11
  start-page: 176
  year: 2009
  end-page: 182
  article-title: Prognostic significance of imaging contrast enhancement for WHO grade II gliomas
  publication-title: Neuro-oncology
– volume: 21
  start-page: 1624
  year: 2003
  end-page: 1636
  article-title: Cost of migration: invasion of malignant gliomas and implications for treatment
  publication-title: J. Clin. Oncol.
– volume: 56
  start-page: 704
  year: 1997
  article-title: The interaction of growth rates and diffusion coefficients in a three‐dimensional mathematical model of gliomas
  publication-title: J. Neuropathol. Exp. Neurol.
– volume: 114
  start-page: 97
  year: 2007
  end-page: 109
  article-title: The 2007 WHO classification of tumours of the central nervous system
  publication-title: Acta Neuropathol.
– volume: 3
  start-page: 93
  year: 2006
  end-page: 100
  article-title: A cellular automaton model for the migration of glioma cells
  publication-title: Phys. Biol.
– volume: 24
  start-page: 131
  year: 1995
  article-title: Resection of gliomas and life expectancy
  publication-title: J. Neurooncol.
– ident: e_1_2_7_18_2
  doi: 10.1016/j.jtbi.2007.10.026
– ident: e_1_2_7_25_2
  doi: 10.1111/j.1365-2184.1995.tb00036.x
– ident: e_1_2_7_30_2
  doi: 10.1097/nen.0b013e31802d9000
– ident: e_1_2_7_45_2
  doi: 10.1111/j.1365-2184.2010.00703.x
– ident: e_1_2_7_34_2
  doi: 10.1088/0031-9155/55/12/001
– ident: e_1_2_7_21_2
  doi: 10.1103/PhysRevE.79.031917
– ident: e_1_2_7_39_2
  doi: 10.1007/s00701-010-0917-x
– ident: e_1_2_7_31_2
  doi: 10.1111/j.1365-2184.1996.tb01580.x
– ident: e_1_2_7_23_2
  doi: 10.1111/j.1365-2184.2009.00631.x
– ident: e_1_2_7_2_2
  doi: 10.1007/s00401-007-0243-4
– ident: e_1_2_7_7_2
  doi: 10.1227/01.neu.0000318159.21731.cf
– ident: e_1_2_7_12_2
  doi: 10.1200/JCO.2003.05.063
– ident: e_1_2_7_4_2
  doi: 10.1212/WNL.0b013e3181e04264
– ident: e_1_2_7_26_2
  doi: 10.1142/S0218339095000836
– ident: e_1_2_7_9_2
  doi: 10.1002/ana.21125
– ident: e_1_2_7_13_2
  doi: 10.1046/j.1365-2184.2000.00177.x
– ident: e_1_2_7_37_2
  doi: 10.1007/s11060-009-9991-4
– ident: e_1_2_7_19_2
  doi: 10.1088/1478-3975/3/2/001
– ident: e_1_2_7_6_2
  doi: 10.2174/157488706778250140
– ident: e_1_2_7_11_2
  doi: 10.1093/neuonc/noq055
– ident: e_1_2_7_14_2
  doi: 10.1002/mrm.20625
– ident: e_1_2_7_33_2
  doi: 10.1088/0031-9155/52/11/023
– ident: e_1_2_7_5_2
  doi: 10.3171/jns.2001.95.5.0735
– ident: e_1_2_7_44_2
  doi: 10.1006/bulm.1998.0042
– ident: e_1_2_7_22_2
  doi: 10.1088/1478-3975/7/4/046013
– ident: e_1_2_7_15_2
  doi: 10.1158/0008-5472.CAN-05-3166
– ident: e_1_2_7_8_2
  doi: 10.1007/s10143-009-0229-x
– ident: e_1_2_7_16_2
  doi: 10.1088/1478-3975/4/4/P01
– ident: e_1_2_7_43_2
  doi: 10.1023/A:1005707203596
– ident: e_1_2_7_41_2
  doi: 10.1215/15228517-2008-066
– ident: e_1_2_7_40_2
  doi: 10.1007/b98868
– ident: e_1_2_7_29_2
  doi: 10.1088/0031-9155/53/4/004
– ident: e_1_2_7_3_2
  doi: 10.3171/jns.1987.66.6.0865
– volume: 72
  start-page: 356
  year: 2007
  ident: e_1_2_7_17_2
  article-title: A mathematical model of glioblastoma tumor spheroid invasion in a three‐dimensional in vitro experiment
  publication-title: Rep. Prog. Phys.
  contributor:
    fullname: Stein AM
– volume-title: Mathematical Biology. II: Spatial Models and Biomedical Applications
  year: 2002
  ident: e_1_2_7_24_2
  doi: 10.1007/b98868
  contributor:
    fullname: Murray JD
– volume: 24
  start-page: 131
  year: 1995
  ident: e_1_2_7_27_2
  article-title: Resection of gliomas and life expectancy
  publication-title: J. Neurooncol.
  contributor:
    fullname: Cook J
– volume: 216
  start-page: 289
  year: 2004
  ident: e_1_2_7_32_2
  article-title: Virtual and real brain tumors: using mathematical modeling to quantify glioma growth and invasion
  publication-title: J. Neurol. Sci.
  contributor:
    fullname: Swanson KR
– ident: e_1_2_7_38_2
  doi: 10.1002/ana.22106
– ident: e_1_2_7_20_2
  doi: 10.1098/rsif.2007.1070
– ident: e_1_2_7_36_2
  doi: 10.1002/ana.20946
– ident: e_1_2_7_42_2
  doi: 10.1007/s10143-008-0128-6
– ident: e_1_2_7_35_2
  doi: 10.1002/ana.10528
– ident: e_1_2_7_28_2
  doi: 10.1097/00005072-199706000-00008
– ident: e_1_2_7_10_2
  doi: 10.1016/j.ncl.2010.03.022
SSID ssj0013176
Score 2.2271302
Snippet Objectives:  Here we present a model aiming to provide an estimate of time from tumour genesis, for grade II gliomas. The model is based on a differential...
Here we present a model aiming to provide an estimate of time from tumour genesis, for grade II gliomas. The model is based on a differential equation...
Abstract Objectives:  Here we present a model aiming to provide an estimate of time from tumour genesis, for grade II gliomas. The model is based on a...
OBJECTIVESHere we present a model aiming to provide an estimate of time from tumour genesis, for grade II gliomas. The model is based on a differential...
Objectives:  Here we present a model aiming to provide an estimate of time from tumour genesis, for grade II gliomas. The model is based on a differential...
SourceID pubmedcentral
proquest
crossref
pubmed
wiley
istex
SourceType Open Access Repository
Aggregation Database
Index Database
Publisher
StartPage 76
SubjectTerms Cell Proliferation
Glioma - pathology
Humans
Models, Biological
Models, Statistical
Neoplasm Grading
Original
Time Factors
SummonAdditionalLinks – databaseName: Wiley Online Library - Core collection (SURFmarket)
  dbid: DR2
  link: http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwrV1Lb9QwEB6hIiQuvB8pD_mAuGWVOPEj3NBC2Va0gqoVvVm247SrhWy13ZUKJ34Cv5FfwoyzG7qlB4S4WXbsyDMe-7P9eQbghcUlplGFTTMn67SUPKQ2V7hV0QFzpFNW0kPh3T05Oix3jsTRkv9Eb2E6_xD9gRtZRpyvycCtO1s38sjQwi3K0hNnpqpsQHgyLxSxu97s898XCnmMM0eAnQDlJVLPlQ2trVTXSejnV8HQP9mUF1FuXKa2bsNk1cGOnTIZLOZu4L9d8v34fyRwB24t0Sx73Q2_u3AttPfgRhff8ut92OmPLBjiTEZx7H9-__El8jfDK0YOPggwYzGdO7Bpw9x4Nj-hxPHM1oFtb7Pjz2OiMD2Aw623B8NRugzekHrcwmRp7nwtBeqa21oHilrlCysy1IxovOd5o4VyBc4fVU2Pc23BKUaFx-mualyjVfEQNtppGx4DCz4TXpY-K5qyrKysrGiE1IUuK66D0gnkK0WZ085Hh7mwt0EZGZKRIRmZKCNznsDLqNG-gp1NiOOmhPm098581KOher8rzUECbKVyg5ZH1ym2DdPFmak49gbhUJ7Ao24E9I1xnkuNP05ArY2N_gNy6r1e0o5PonNvWVYSIVsCIqr-rztkhh_2MbH5j_WewE3M5R0f_SlszGeL8Azh1tw9j4b0C17xG1E
  priority: 102
  providerName: Wiley-Blackwell
Title Improving the time-machine: estimating date of birth of grade II gliomas
URI https://api.istex.fr/ark:/67375/WNG-Q8HC7LM6-T/fulltext.pdf
https://onlinelibrary.wiley.com/doi/abs/10.1111%2Fj.1365-2184.2011.00790.x
https://www.ncbi.nlm.nih.gov/pubmed/22168136
https://search.proquest.com/docview/920370611
https://pubmed.ncbi.nlm.nih.gov/PMC6496223
Volume 45
hasFullText 1
inHoldings 1
isFullTextHit
isPrint
link http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwnV3NTtwwEB7xI6ReqhZoG9oiH6rewiZO_JPeqlXpglgECFRuluM4sCqbRdtFglsfoc_YJ-mMs9lC20PFLYoTJ54ZZ76JP88AvLPoYmqV2TgpZRXnkvvYpgpDFe3xjCyVlbRReHgoB2f5_rk4XwLR7YUJpH1Xjnaaq_FOM7oM3Mrrset1PLHe0bAv80KiW-stwzIaaBeid0sHaagoR9CcoOMf9J1A6sKoZp68M1FFEnJRc55KnYZEzb_d0ypJ-vZf2PNvCuV9aBt80-4zeDoHlexj-_LPYck367DWlpm824D9xZ8DhnCPUTn5n99_jAON0n9glGeDcCs2U_jPJjUrR9PZJR1cTG3l2d4eu7gaEZNoE852P532B_G8hkLsMJJI4rR0lRQocm4r7al4lMusSDKlRe0cT2stVJnhNC4q2iNrM06lIhx-dYq6rLXKXsBKM2n8K2DeJcLJ3CVZneeFlYUVtZA603nBtVc6grQTnbluU2WYeyEGSt6Q5A1J3gTJm9sI3gcZL26w069ENVPCfDn8bI71oK8OhtKcRsA6JRicALSqYRs_uflmCo6jQVSSRvCy1cmis06pEagH2lpcQLm1H7agyYUc23MTi0AEvf73gEz_6AQPth79xNfwBPvjLTH8DazMpjf-LeKeWbmNiP-Ebwdr_wVsXvxA
link.rule.ids 230,315,730,783,787,888,1378,27936,27937,46306,46730,53804,53806
linkProvider National Library of Medicine
linkToHtml http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwnV1bb9MwFD4amxC8jDtk3PyAeEubmy_hDVWMdrTVQB3szbIdZ6u2plPXShtP-wn7jfwSjp2mbIMH4C2KE0cnny_fsT-fA_BG4RRT8lSFkWZFmLHEhirm6KoIi3eY5oq5g8KDIevuZTv7dH8NaHMWxov2jR63quNJqxofem3lycS0G51Ye3fQYVnOcFpr34IN7K9R1jjpzeZB7HPKOXLuyOMNAY-XdaFfswzfGfE88tGokyRmIvahmn9NUBvuX5_9iX3-LqK8Sm797LR9D742dtWilKPWYq5b5vuNkI__bPh92FzyVfK-Ln4Aa7Z6CLfrDJbnj2BntShBkEkSl6n-x8XlxCs07TviQng4SozFbmWBTEuix7P5obs4mKnCkl6PHByPnUjpMextfxh1uuEyPUNo0EmJwlibglFEM1GFsC4vlUkVjVIuaGlMEpeCcp3iCJEX7vitShOXhcLggJaXuhQ8fQLr1bSyz4BYE1HDMhOlZZbliuWKlpSJVGR5IiwXAcQNJvKkjsIhr3gvCKl0kEoHqfSQyrMA3nrwVi-o2ZFTsXEqvw0_ys-i2-H9AZOjAEiDrsS-5TZMVGWni1OZJ2gNEp44gKc12KvKmtYSAL_WDFYPuLDd10sQVB--ewliANQ3mL82SHZ2v-DF1n9_8TXc6Y4GfdnvDT89h7tYd1Lrz1_A-ny2sC-RXs31K9-ZfgJxVR1c
linkToPdf http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwnV3LbhMxFL2CViA25Q3D0wvEbpJ5-THsUCAkpYkCakXFxrI9njZqM4nSRCqs-AS-kS_h2pMJaWGBurPGHo88x49z7eN7AV4pXGJKnqow0qwIM5bYUMUcTRVh8QnTXDF3UXgwZL2DbPeQHm6E-vKifaPHrep00qrGx15bOZuYdqMTa48GHZblDJe19qwo29dhG8dsxBpDvTlAiH1cOUfQHYG8JOLx0i60bVYuPCOeR94jdZLETMTeXfOfRWrb_e_zfzHQv4WUmwTXr1Dd2_C1aVstTDlpLRe6Zb5fcvt4pcbfgZ0VbyVv6yJ34Zqt7sGNOpLlt_uwu96cIMgoiYtY_-vHz4lXato3xLnycNQYs90OA5mWRI_ni2OXOJqrwpJ-nxydjp1Y6QEcdN_vd3rhKkxDaNBYicJYm4JRRDVRhbAuPpVJFY1SLmhpTBKXgnKd4kyRF-4arkoTF43C4MSWl7oUPH0IW9W0so-BWBNRwzITpWWW5YrlipaUiVRkeSIsFwHEDS5yVnvjkBtWDMIqHazSwSo9rPI8gNcewPULan7i1Gycyi_DD_KT6HX43oDJ_QBIg7DEMeYOTlRlp8szmSfYGiQ-cQCPasDXlTU9JgB-oSusCzj33RdzEFjvxnsFZADUd5r_bpDsjD5j4smVv_gSbo7edeVef_jxKdzCqpNahv4MthbzpX2OLGuhX_jx9BuOoh_c
openUrl ctx_ver=Z39.88-2004&ctx_enc=info%3Aofi%2Fenc%3AUTF-8&rfr_id=info%3Asid%2Fsummon.serialssolutions.com&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Ajournal&rft.genre=article&rft.atitle=Improving+the+time-machine%3A+estimating+date+of+birth+of+grade+II+gliomas&rft.jtitle=Cell+proliferation&rft.au=Gerin%2C+C&rft.au=Pallud%2C+J&rft.au=Grammaticos%2C+B&rft.au=Mandonnet%2C+E&rft.date=2012-02-01&rft.eissn=1365-2184&rft.volume=45&rft.issue=1&rft.spage=76&rft_id=info:doi/10.1111%2Fj.1365-2184.2011.00790.x&rft_id=info%3Apmid%2F22168136&rft.externalDocID=22168136
thumbnail_l http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/lc.gif&issn=0960-7722&client=summon
thumbnail_m http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/mc.gif&issn=0960-7722&client=summon
thumbnail_s http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/sc.gif&issn=0960-7722&client=summon